Assessment of lemon juice adulteration by targeted screening using LC-UV-MS and untargeted screening using UHPLC-QTOF/MS with machine learning.

The aim of this work was to develop an approach combining LC-MS-based metabolomics and machine learning to distinguish between and predict authentic and adulterated lemon juices. A targeted screening of six major flavonoids was first conducted using ultraviolet ion trap MS. To improve the prediction accuracy, an untargeted methodology was carried out using UHPLC-QTOF/MS. Based on the acquired metabolic profiles, both PCA and PLS-DA were conducted. Results exhibited a cluster pattern and a separation potential between authentic and adulterated samples. Five machine learning models were then developed to further analyze the data. The model of support vector machine achieved the highest prediction power, with accuracy up to 96.7 ± 7.5% for the cross-validation set and 100% for the testing set. In addition, 79 characteristic m/z were tentatively identified. This work demonstrated that untargeted screening coupled with machine learning models can be a powerful tool to facilitate detection of lemon juice adulteration.